Research on feature point extraction and matching machine learning method based on light field imaging

Abstract

At present, there are many methods to realize the matching of specified images with features, and the basic components include image feature point detection, feature description, and image matching. Based on this background, this article has done different research and exploration around these three aspects. The image feature point detection method is firstly studied, which commonly include image edge information-based feature detection method, corner information-based detection method, and various interest operators. However, all of the traditional detection methods are involved in problems of large computation burden and time consumption. In order to solve this problem, a feature detection method based on image grayscale information-FAST operator is used in this paper, which is combined with decision tree theory to effectively improve the speed of extracting image feature points. Then, the feature point description method BRIEF operator is studied, which is a local expression of detected image feature points based on descriptors. Since the descriptor does not have rotation invariance, the detection operator is endowed by a direction that is proposed in this paper, and then the local feature description is conducted on the feature descriptor to generate a binary string array containing direction information. Finally, the feature matching machine learning method is analyzed, and the nearest search method is used to find the nearest feature point pair in Euclidean distance, of which the calculation burden is small. The simulation results show that the proposed nearest neighbor search and matching machine learning algorithm has higher matching accuracy and faster calculation speed compared with the classical feature matching algorithm, which has great advantages in processing a large number of array images captured by the light field camera.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

References

  1. 1.

    Kim SS, Sohn KH, Savaljev V et al (2001) A full parallax three-dimensional imaging system based on a point light source array. Jpn J Appl Phys 40(8):4913–4915

    Article  Google Scholar 

  2. 2.

    Choi H, Wadduwage DN, Tu TY et al (2015) Three-dimensional image cytometer based on widefield structured light microscopy and high-speed remote depth scanning. Cytometry Part A 87(1):49–60

    Article  Google Scholar 

  3. 3.

    Fang SY, Fang JJ (2011) Automatic head and facial feature extraction based on geometry variations. Comput Aided Des 43(12):1729–1739

    Article  Google Scholar 

  4. 4.

    Kim HS, Jeong KM, Hong SI et al (2012) Analysis of image distortion based on light ray field by multi-view and horizontal parallax only integral imaging display. Opt Express 20(21):23755

    Article  Google Scholar 

  5. 5.

    Yin HX, Zhu CR, Shen Y et al (2014) Enhanced light extraction in n-GaN-based light-emitting diodes with three-dimensional semi-spherical structure. Appl Phys Lett 104(6):1274

    Google Scholar 

  6. 6.

    Zhang M, Geng Z, Pei R et al (2017) Three-dimensional light field microscope based on a lenslet array. Opt Commun 403:133–142

    Article  Google Scholar 

  7. 7.

    Young EF, Rannou P, Mckay CP et al (2007) A three-dimensional map of titan’s tropospheric haze distribution based on [ITAL] Hubble Space Telescope [/ITAL] imaging. Astron J 123(6):3473

    Article  Google Scholar 

  8. 8.

    Kumar RP, Albregtsen F, Reimers M et al (2015) Three-dimensional blood vessel segmentation and centerline extraction based on two-dimensional cross-section analysis. Ann Biomed Eng 43(5):1223–1234

    Article  Google Scholar 

  9. 9.

    Arimura H, Li Q, Korogi Y et al (2006) Computerized detection of intracranial aneurysms for three-dimensional MR angiography: feature extraction of small protrusions based on a shape-based difference image technique. Med Phys 33(2):394–401

    Article  Google Scholar 

  10. 10.

    Xie C, Guan W, Wu X, et al. (2018) The LED-ID detection and recognition method based on visible light positioning using proximity method. IEEE Photonics J 99:1.

    Google Scholar 

  11. 11.

    Asakura T, Sakata K, Date Y et al (2018) Regional feature extraction of various fishes based on chemical and microbial variable selection using machine learning. Anal Methods. https://doi.org/10.1039/C8AY00377G

    Article  Google Scholar 

  12. 12.

    Shi J, Wang Y, Chen T et al (2018) Automatic evaluation of traumatic brain injury based on terahertz imaging with machine learning. Opt Express 26(5):6371–6381

    Article  Google Scholar 

  13. 13.

    Ertuğrul ÖF, Tağluk ME (2017) A novel machine learning method based on generalized behavioral learning theory. Neural Comput Appl 28(12):3921–3939

    Article  Google Scholar 

  14. 14.

    Sun Q, Zhang Y, Wang J, et al. (2017) An improved FAST feature extraction based on RANSAC method of vision/SINS integrated navigation system in GNSS-denied environments. Adv Space Res 60(12)

    Article  Google Scholar 

  15. 15.

    Nagayama T, Mancini RC, Florido R et al (2012) Investigation of a polychromatic tomography method for the extraction of the three-dimensional spatial structure of implosion core plasmas. Phys Plasmas 19(8):139

    Article  Google Scholar 

  16. 16.

    Rosalesortega FF, Arribas S, Colina L (2012) Integrated spectra extraction based on signal-to-noise optimization using integral field spectroscopy. Astron Astrophys 539(1):307–316

    Google Scholar 

  17. 17.

    Ukwatta E, Awad J, Ward AD et al (2011) Three-dimensional ultrasound of carotid atherosclerosis: semiautomated segmentation using a level set-based method. Med Phys 38(5):2479–2493

    Article  Google Scholar 

  18. 18.

    Wischgoll T, Choy JS, Ritman EL et al (2008) Validation of image-based method for extraction of coronary morphometry. Ann Biomed Eng 36(3):356–368

    Article  Google Scholar 

  19. 19.

    Du Q, Liu R, Pan Y (2017) Depth extraction for a structured light system based on mismatched image pair rectification using a virtual camera. IET Image Proc 11(11):1086–1093

    Article  Google Scholar 

  20. 20.

    Iglesias JE, Liu CY, Thompson PM et al (2011) Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 30(9):1617–1634

    Article  Google Scholar 

  21. 21.

    Tariq F, Yufit V, Kishimoto M et al (2014) Three-dimensional high resolution X-ray imaging and quantification of lithium ion battery mesocarbon microbead anodes. J Power Sources 248(4):1014–1020

    Article  Google Scholar 

  22. 22.

    Fadeyev V, Haber C, Maul C et al (2004) Reconstruction of mechanically recorded sound from an edison cylinder using three dimensional non-contact optical surface metrology. J Audio Eng Soc 53(6):485–508

    Google Scholar 

  23. 23.

    Kühmstedt P, Schreiber P, Notni G (2014) Array projection of aperiodic sinusoidal fringes for high-speed three-dimensional shape measurement. Opt Eng 53(11):112208

    Article  Google Scholar 

  24. 24.

    Geilhufe J, Tieg C, Pfau B et al (2014) Extracting depth information of 3-dimensional structures from a single-view X-ray Fourier-transform hologram. Opt Express 22(21):24959–24969

    Article  Google Scholar 

  25. 25.

    Yelnik J, Bardinet E, Dormont D et al (2007) A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 34(2):618–638

    Article  Google Scholar 

  26. 26.

    Liu B, Cheng HD, Huang J et al (2010) Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recogn 43(1):280–298

    Article  Google Scholar 

  27. 27.

    Borgnia MJ, Subramaniam S, Milne JLS (2008) Three-dimensional imaging of the highly bent architecture of bdellovibrio bacteriovorus by using cryo-electron tomography. J Bacteriol 190(7):2588

    Article  Google Scholar 

  28. 28.

    Chandar R, Leitherer C, Tremonti C et al (2003) The stellar content of henize 2-10 from space telescope imaging spectrograph ultraviolet spectroscopy. Astrophys J 586(2):939

    Article  Google Scholar 

  29. 29.

    Stal C, Tack F, Maeyer PD et al (2013) Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area—a comparative study. Int J Remote Sens 34(4):1087–1110

    Article  Google Scholar 

  30. 30.

    Yu Z, Holst MJ, Hayashi T et al (2008) Three-dimensional geometric modeling of membrane-bound organelles in ventricular myocytes: bridging the gap between microscopic imaging and mathematical simulation. J Struct Biol 164(3):304–313

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yue Wu.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, Y. Research on feature point extraction and matching machine learning method based on light field imaging. Neural Comput & Applic 31, 8157–8169 (2019). https://doi.org/10.1007/s00521-018-3962-7

Download citation

Keywords

  • Image matching
  • Machine learning
  • Nearest neighbor search
  • Light field imaging